FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313587923> ?p ?o ?g. }

• W4313587923 endingPage "186" @default.
• W4313587923 startingPage "186" @default.
• W4313587923 abstract "Visual pollution (VP) is the deterioration or disruption of natural and man-made landscapes that ruins the aesthetic appeal of an area. It also refers to physical elements that limit the movability of people on public roads, such as excavation barriers, potholes, and dilapidated sidewalks. In this paper, an end-to-end visual pollution prediction (VPP) framework based on a deep active learning (DAL) approach is proposed to simultaneously detect and classify visual pollutants from whole public road images. The proposed framework is architected around the following steps: real VP dataset collection, pre-processing, a DAL approach for automatic data annotation, data splitting as well as augmentation, and simultaneous VP detection and classification. This framework is designed to predict VP localization and classify it into three categories: excavation barriers, potholes, and dilapidated sidewalks. A real dataset with 34,460 VP images was collected from various regions across the Kingdom of Saudi Arabia (KSA) via the Ministry of Municipal and Rural Affairs and Housing (MOMRAH), and this was used to develop and fine-tune the proposed artificial intelligence (AI) framework via the use of five AI predictors: MobileNetSSDv2, EfficientDet, Faster RCNN, Detectron2, and YOLO. The proposed VPP-based YOLO framework outperforms competitor AI predictors with superior prediction performance at 89% precision, 88% recall, 89% F1-score, and 93% mAP. The DAL approach plays a crucial role in automatically annotating the VP images and supporting the VPP framework to improve prediction performance by 18% precision, 27% recall, and 25% mAP. The proposed VPP framework is able to simultaneously detect and classify distinct visual pollutants from annotated images via the DAL strategy. This technique is applicable for real-time monitoring applications." @default.
• W4313587923 created "2023-01-06" @default.
• W4313587923 creator A5011141471 @default.
• W4313587923 creator A5022092645 @default.
• W4313587923 creator A5038242344 @default.
• W4313587923 creator A5043544976 @default.
• W4313587923 creator A5064365675 @default.
• W4313587923 creator A5087525608 @default.
• W4313587923 date "2022-12-29" @default.
• W4313587923 modified "2023-09-26" @default.
• W4313587923 title "VPP: Visual Pollution Prediction Framework Based on a Deep Active Learning Approach Using Public Road Images" @default.
• W4313587923 cites W2069182664 @default.
• W4313587923 cites W2109255472 @default.
• W4313587923 cites W2194775991 @default.
• W4313587923 cites W2290960045 @default.
• W4313587923 cites W2570343428 @default.
• W4313587923 cites W2793956967 @default.
• W4313587923 cites W2803575519 @default.
• W4313587923 cites W2809504579 @default.
• W4313587923 cites W2889992416 @default.
• W4313587923 cites W2937912229 @default.
• W4313587923 cites W2959310882 @default.
• W4313587923 cites W2963037989 @default.
• W4313587923 cites W2963446712 @default.
• W4313587923 cites W2963857746 @default.
• W4313587923 cites W2964308596 @default.
• W4313587923 cites W2997999933 @default.
• W4313587923 cites W3001083904 @default.
• W4313587923 cites W3033750579 @default.
• W4313587923 cites W3034971973 @default.
• W4313587923 cites W3041570645 @default.
• W4313587923 cites W3088936717 @default.
• W4313587923 cites W3094292917 @default.
• W4313587923 cites W3110769980 @default.
• W4313587923 cites W3129052952 @default.
• W4313587923 cites W3137588588 @default.
• W4313587923 cites W3138516171 @default.
• W4313587923 cites W3166173412 @default.
• W4313587923 cites W3178920961 @default.
• W4313587923 cites W3184359986 @default.
• W4313587923 cites W3210586215 @default.
• W4313587923 cites W4206765025 @default.
• W4313587923 cites W4220681577 @default.
• W4313587923 cites W4309457179 @default.
• W4313587923 cites W639708223 @default.
• W4313587923 doi "https://doi.org/10.3390/math11010186" @default.
• W4313587923 hasPublicationYear "2022" @default.
• W4313587923 type Work @default.
• W4313587923 citedByCount "2" @default.
• W4313587923 countsByYear W43135879232023 @default.
• W4313587923 crossrefType "journal-article" @default.
• W4313587923 hasAuthorship W4313587923A5011141471 @default.
• W4313587923 hasAuthorship W4313587923A5022092645 @default.
• W4313587923 hasAuthorship W4313587923A5038242344 @default.
• W4313587923 hasAuthorship W4313587923A5043544976 @default.
• W4313587923 hasAuthorship W4313587923A5064365675 @default.
• W4313587923 hasAuthorship W4313587923A5087525608 @default.
• W4313587923 hasBestOaLocation W43135879231 @default.
• W4313587923 hasConcept C100660578 @default.
• W4313587923 hasConcept C108583219 @default.
• W4313587923 hasConcept C119857082 @default.
• W4313587923 hasConcept C134306372 @default.
• W4313587923 hasConcept C138885662 @default.
• W4313587923 hasConcept C154945302 @default.
• W4313587923 hasConcept C169258074 @default.
• W4313587923 hasConcept C27206212 @default.
• W4313587923 hasConcept C2776321320 @default.
• W4313587923 hasConcept C33923547 @default.
• W4313587923 hasConcept C41008148 @default.
• W4313587923 hasConcept C41895202 @default.
• W4313587923 hasConcept C521751864 @default.
• W4313587923 hasConcept C77618280 @default.
• W4313587923 hasConceptScore W4313587923C100660578 @default.
• W4313587923 hasConceptScore W4313587923C108583219 @default.
• W4313587923 hasConceptScore W4313587923C119857082 @default.
• W4313587923 hasConceptScore W4313587923C134306372 @default.
• W4313587923 hasConceptScore W4313587923C138885662 @default.
• W4313587923 hasConceptScore W4313587923C154945302 @default.
• W4313587923 hasConceptScore W4313587923C169258074 @default.
• W4313587923 hasConceptScore W4313587923C27206212 @default.
• W4313587923 hasConceptScore W4313587923C2776321320 @default.
• W4313587923 hasConceptScore W4313587923C33923547 @default.
• W4313587923 hasConceptScore W4313587923C41008148 @default.
• W4313587923 hasConceptScore W4313587923C41895202 @default.
• W4313587923 hasConceptScore W4313587923C521751864 @default.
• W4313587923 hasConceptScore W4313587923C77618280 @default.
• W4313587923 hasFunder F4320322804 @default.
• W4313587923 hasIssue "1" @default.
• W4313587923 hasLocation W43135879231 @default.
• W4313587923 hasOpenAccess W4313587923 @default.
• W4313587923 hasPrimaryLocation W43135879231 @default.
• W4313587923 hasRelatedWork W2968586400 @default.
• W4313587923 hasRelatedWork W3211546796 @default.
• W4313587923 hasRelatedWork W4223564025 @default.
• W4313587923 hasRelatedWork W4223943233 @default.
• W4313587923 hasRelatedWork W4281616679 @default.
• W4313587923 hasRelatedWork W4312200629 @default.
• W4313587923 hasRelatedWork W4360585206 @default.

• next